soren.faurby at biology.au.dk wrote:
> Full_Name: Søren Faurby
> Version: 2.4.1 and 2.7.2
> OS:
> Submission from: (NULL) (192.38.46.92)
>>> There appear to be a bug in the estimation of significance in the binomial model
> in GLM. This bug apparently appears when the correlation between two variables
> is to strong.
>> Such as this dummy example
> c(0,0,0,0,0,1,1,1,1,1)->a
> a->b
> m1<-glm(a~b, binomial)
> summary(m1)
>> It is sufficient that all 1's correspond to 1's such as this example
>> c(0,0,0,0,0,1,1,1,1,1)->a
> c(0,0,0,0,1,1,1,1,1,1)->c
> m1<-glm(a~c, binomial)
> summary(m1)
That's not a bug, just the way things work. When the algorithm diverges,
as seen by the huge Std.Error, Wald tests (z) are unreliable. (Notice
that the log OR in an a vs. c table is infinite whichever way you turn
it.) The likelihood ratio test (as in drop1(m1, test="Chisq")) is
somewhat less unreliable, but in these small examples, still quite some
distance from the table based approaches of fisher.test(a,c) and
chisq.test(a,c).
>> I hope that this message is understandable.
>> Kind regards, Søren
>> ______________________________________________
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